Robust variable selection for the varying coefficient model based on composite <italic>L</italic> <sub>1</sub>--<italic>L</italic> <sub>2</sub> regression
The varying coefficient model (VCM) is an important generalization of the linear regression model and many existing estimation procedures for VCM were built on <italic>L</italic> <sub>2</sub> loss, which is popular for its mathematical beauty but is not robust to non-normal errors and outliers. In this paper, we address the problem of both robustness and efficiency of estimation and variable selection procedure based on the convex combined loss of <italic>L</italic> <sub>1</sub> and <italic>L</italic> <sub>2</sub> instead of only quadratic loss for VCM. By using local linear modeling method, the asymptotic normality of estimation is driven and a useful selection method is proposed for the weight of composite <italic>L</italic> <sub>1</sub> and <italic>L</italic> <sub>2</sub>. Then the variable selection procedure is given by combining local kernel smoothing with adaptive group LASSO. With appropriate selection of tuning parameters by Bayesian information criterion (BIC) the theoretical properties of the new procedure, including consistency in variable selection and the oracle property in estimation, are established. The finite sample performance of the new method is investigated through simulation studies and the analysis of body fat data. Numerical studies show that the new method is better than or at least as well as the least square-based method in terms of both robustness and efficiency for variable selection.
